کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6903184 | 1446751 | 2018 | 43 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Evolutionary algorithms based synthesis of low sidelobe hexagonal arrays
ترجمه فارسی عنوان
سنتز مبتنی بر الگوریتم های تکاملی آرایه های شش ضلعی کوچک کمربند
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کلمات کلیدی
Firefly algorithm - الگوریتم FireflyDifferential evolution - الگوریتم تکاملی تفاضلیEvolutionary algorithms - الگوریتم های تکاملیReal coded genetic algorithm - الگوریتم ژنتیک واقعی کدگذاری شده استFlower pollination algorithm - الگوریتم گرده افشانی گلParticle swarm optimization - بهینه سازی ازدحام ذراتdirectivity - جهت گیریConstraint handling - دست زدن به محدودیتInterference suppression - سرکوب تداخلات
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
علوم کامپیوتر (عمومی)
چکیده انگلیسی
In this work a procedure namely Findpeaks2 is proposed to detect the maximum sidelobe level (SLL) from the samples of three dimensional radiation pattern. This procedure detects all sidelobe peaks form the samples of the radiation pattern in the entire visible region. For illustration, a low sidelobe radiation pattern synthesis problem is formulated for two concentric regular hexagonal antenna array (CRHAA) geometries, having 6- and 8- rings. To verify the extent of applicability of the proposed procedure, both broadside and scanned array configurations are considered. Feed current amplitudes are considered as the optimizing variables. Two variations of current distributions are considered, i) identical feed for all the elements on a ring (hence the one variable per ring needs to be optimized), and ii) asymmetric excitation distribution (set of excitation amplitude of all elements as optimizing variables). The design objective has been considered to optimize the radiation patterns with very low interference from the entire sidelobe region. To restrict the fall of directivity value, a constraint on the lower limit of directivity value is considered. The impacts of symmetry and the constraint on directivity on the search of these algorithms are studied. Evolutionary algorithms like Real Coded Genetic Algorithm (RGA), Firefly Algorithm (FFA), Flower Pollination Algorithm (FPA), an adaptive variant of Particle Swarm Optimization Algorithm namely (APSO), and two recently proposed variants of DE namely Exponentially Weighted Moving Average Differential Evolution (EWMA-DE), and Differential Evolution with Individual Dependent Mechanism (IDE) are employed for this pattern optimization problem.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Swarm and Evolutionary Computation - Volume 38, February 2018, Pages 139-157
Journal: Swarm and Evolutionary Computation - Volume 38, February 2018, Pages 139-157
نویسندگان
Sudipta Das, Rajesh Bera, Durbadal Mandal, Sakti Prasad Ghoshal, Rajib Kar,